Upload crepe.py

modules/crepe.py

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+from typing import Optional,Union
+try:
+    from typing import Literal
+except Exception as e:
+    from typing_extensions import Literal
+import numpy as np
+import torch
+import torchcrepe
+from torch import nn
+from torch.nn import functional as F
+import scipy
+
+#from:https://github.com/fishaudio/fish-diffusion
+
+def repeat_expand(
+    content: Union[torch.Tensor, np.ndarray], target_len: int, mode: str = "nearest"
+):
+    """Repeat content to target length.
+    This is a wrapper of torch.nn.functional.interpolate.
+
+    Args:
+        content (torch.Tensor): tensor
+        target_len (int): target length
+        mode (str, optional): interpolation mode. Defaults to "nearest".
+
+    Returns:
+        torch.Tensor: tensor
+    """
+
+    ndim = content.ndim
+
+    if content.ndim == 1:
+        content = content[None, None]
+    elif content.ndim == 2:
+        content = content[None]
+
+    assert content.ndim == 3
+
+    is_np = isinstance(content, np.ndarray)
+    if is_np:
+        content = torch.from_numpy(content)
+
+    results = torch.nn.functional.interpolate(content, size=target_len, mode=mode)
+
+    if is_np:
+        results = results.numpy()
+
+    if ndim == 1:
+        return results[0, 0]
+    elif ndim == 2:
+        return results[0]
+
+
+class BasePitchExtractor:
+    def __init__(
+        self,
+        hop_length: int = 512,
+        f0_min: float = 50.0,
+        f0_max: float = 1100.0,
+        keep_zeros: bool = True,
+    ):
+        """Base pitch extractor.
+
+        Args:
+            hop_length (int, optional): Hop length. Defaults to 512.
+            f0_min (float, optional): Minimum f0. Defaults to 50.0.
+            f0_max (float, optional): Maximum f0. Defaults to 1100.0.
+            keep_zeros (bool, optional): Whether keep zeros in pitch. Defaults to True.
+        """
+
+        self.hop_length = hop_length
+        self.f0_min = f0_min
+        self.f0_max = f0_max
+        self.keep_zeros = keep_zeros
+
+    def __call__(self, x, sampling_rate=44100, pad_to=None):
+        raise NotImplementedError("BasePitchExtractor is not callable.")
+
+    def post_process(self, x, sampling_rate, f0, pad_to):
+        if isinstance(f0, np.ndarray):
+            f0 = torch.from_numpy(f0).float().to(x.device)
+
+        if pad_to is None:
+            return f0
+
+        f0 = repeat_expand(f0, pad_to)
+
+        if self.keep_zeros:
+            return f0
+        
+        vuv_vector = torch.zeros_like(f0)
+        vuv_vector[f0 > 0.0] = 1.0
+        vuv_vector[f0 <= 0.0] = 0.0
+        
+        # 去掉0频率, 并线性插值
+        nzindex = torch.nonzero(f0).squeeze()
+        f0 = torch.index_select(f0, dim=0, index=nzindex).cpu().numpy()
+        time_org = self.hop_length / sampling_rate * nzindex.cpu().numpy()
+        time_frame = np.arange(pad_to) * self.hop_length / sampling_rate
+
+        if f0.shape[0] <= 0:
+            return torch.zeros(pad_to, dtype=torch.float, device=x.device),torch.zeros(pad_to, dtype=torch.float, device=x.device)
+
+        if f0.shape[0] == 1:
+            return torch.ones(pad_to, dtype=torch.float, device=x.device) * f0[0],torch.ones(pad_to, dtype=torch.float, device=x.device)
+    
+        # 大概可以用 torch 重写?
+        f0 = np.interp(time_frame, time_org, f0, left=f0[0], right=f0[-1])
+        vuv_vector = vuv_vector.cpu().numpy()
+        vuv_vector = np.ceil(scipy.ndimage.zoom(vuv_vector,pad_to/len(vuv_vector),order = 0))
+        
+        return f0,vuv_vector
+
+
+class MaskedAvgPool1d(nn.Module):
+    def __init__(
+        self, kernel_size: int, stride: Optional[int] = None, padding: Optional[int] = 0
+    ):
+        """An implementation of mean pooling that supports masked values.
+
+        Args:
+            kernel_size (int): The size of the median pooling window.
+            stride (int, optional): The stride of the median pooling window. Defaults to None.
+            padding (int, optional): The padding of the median pooling window. Defaults to 0.
+        """
+
+        super(MaskedAvgPool1d, self).__init__()
+        self.kernel_size = kernel_size
+        self.stride = stride or kernel_size
+        self.padding = padding
+
+    def forward(self, x, mask=None):
+        ndim = x.dim()
+        if ndim == 2:
+            x = x.unsqueeze(1)
+
+        assert (
+            x.dim() == 3
+        ), "Input tensor must have 2 or 3 dimensions (batch_size, channels, width)"
+
+        # Apply the mask by setting masked elements to zero, or make NaNs zero
+        if mask is None:
+            mask = ~torch.isnan(x)
+
+        # Ensure mask has the same shape as the input tensor
+        assert x.shape == mask.shape, "Input tensor and mask must have the same shape"
+
+        masked_x = torch.where(mask, x, torch.zeros_like(x))
+        # Create a ones kernel with the same number of channels as the input tensor
+        ones_kernel = torch.ones(x.size(1), 1, self.kernel_size, device=x.device)
+
+        # Perform sum pooling
+        sum_pooled = nn.functional.conv1d(
+            masked_x,
+            ones_kernel,
+            stride=self.stride,
+            padding=self.padding,
+            groups=x.size(1),
+        )
+
+        # Count the non-masked (valid) elements in each pooling window
+        valid_count = nn.functional.conv1d(
+            mask.float(),
+            ones_kernel,
+            stride=self.stride,
+            padding=self.padding,
+            groups=x.size(1),
+        )
+        valid_count = valid_count.clamp(min=1)  # Avoid division by zero
+
+        # Perform masked average pooling
+        avg_pooled = sum_pooled / valid_count
+
+        # Fill zero values with NaNs
+        avg_pooled[avg_pooled == 0] = float("nan")
+
+        if ndim == 2:
+            return avg_pooled.squeeze(1)
+
+        return avg_pooled
+
+
+class MaskedMedianPool1d(nn.Module):
+    def __init__(
+        self, kernel_size: int, stride: Optional[int] = None, padding: Optional[int] = 0
+    ):
+        """An implementation of median pooling that supports masked values.
+
+        This implementation is inspired by the median pooling implementation in
+        https://gist.github.com/rwightman/f2d3849281624be7c0f11c85c87c1598
+
+        Args:
+            kernel_size (int): The size of the median pooling window.
+            stride (int, optional): The stride of the median pooling window. Defaults to None.
+            padding (int, optional): The padding of the median pooling window. Defaults to 0.
+        """
+
+        super(MaskedMedianPool1d, self).__init__()
+        self.kernel_size = kernel_size
+        self.stride = stride or kernel_size
+        self.padding = padding
+
+    def forward(self, x, mask=None):
+        ndim = x.dim()
+        if ndim == 2:
+            x = x.unsqueeze(1)
+
+        assert (
+            x.dim() == 3
+        ), "Input tensor must have 2 or 3 dimensions (batch_size, channels, width)"
+
+        if mask is None:
+            mask = ~torch.isnan(x)
+
+        assert x.shape == mask.shape, "Input tensor and mask must have the same shape"
+
+        masked_x = torch.where(mask, x, torch.zeros_like(x))
+
+        x = F.pad(masked_x, (self.padding, self.padding), mode="reflect")
+        mask = F.pad(
+            mask.float(), (self.padding, self.padding), mode="constant", value=0
+        )
+
+        x = x.unfold(2, self.kernel_size, self.stride)
+        mask = mask.unfold(2, self.kernel_size, self.stride)
+
+        x = x.contiguous().view(x.size()[:3] + (-1,))
+        mask = mask.contiguous().view(mask.size()[:3] + (-1,)).to(x.device)
+
+        # Combine the mask with the input tensor
+        #x_masked = torch.where(mask.bool(), x, torch.fill_(torch.zeros_like(x),float("inf")))
+        x_masked = torch.where(mask.bool(), x, torch.FloatTensor([float("inf")]).to(x.device))
+
+        # Sort the masked tensor along the last dimension
+        x_sorted, _ = torch.sort(x_masked, dim=-1)
+
+        # Compute the count of non-masked (valid) values
+        valid_count = mask.sum(dim=-1)
+
+        # Calculate the index of the median value for each pooling window
+        median_idx = (torch.div((valid_count - 1), 2, rounding_mode='trunc')).clamp(min=0)
+
+        # Gather the median values using the calculated indices
+        median_pooled = x_sorted.gather(-1, median_idx.unsqueeze(-1).long()).squeeze(-1)
+
+        # Fill infinite values with NaNs
+        median_pooled[torch.isinf(median_pooled)] = float("nan")
+        
+        if ndim == 2:
+            return median_pooled.squeeze(1)
+
+        return median_pooled
+
+
+class CrepePitchExtractor(BasePitchExtractor):
+    def __init__(
+        self,
+        hop_length: int = 512,
+        f0_min: float = 50.0,
+        f0_max: float = 1100.0,
+        threshold: float = 0.05,
+        keep_zeros: bool = False,
+        device = None,
+        model: Literal["full", "tiny"] = "full",
+        use_fast_filters: bool = True,
+    ):
+        super().__init__(hop_length, f0_min, f0_max, keep_zeros)
+
+        self.threshold = threshold
+        self.model = model
+        self.use_fast_filters = use_fast_filters
+        self.hop_length = hop_length
+        if device is None:
+            self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        else:
+            self.dev = torch.device(device)
+        if self.use_fast_filters:
+            self.median_filter = MaskedMedianPool1d(3, 1, 1).to(device)
+            self.mean_filter = MaskedAvgPool1d(3, 1, 1).to(device)
+
+    def __call__(self, x, sampling_rate=44100, pad_to=None):
+        """Extract pitch using crepe.
+
+
+        Args:
+            x (torch.Tensor): Audio signal, shape (1, T).
+            sampling_rate (int, optional): Sampling rate. Defaults to 44100.
+            pad_to (int, optional): Pad to length. Defaults to None.
+
+        Returns:
+            torch.Tensor: Pitch, shape (T // hop_length,).
+        """
+
+        assert x.ndim == 2, f"Expected 2D tensor, got {x.ndim}D tensor."
+        assert x.shape[0] == 1, f"Expected 1 channel, got {x.shape[0]} channels."
+
+        x = x.to(self.dev)
+        f0, pd = torchcrepe.predict(
+            x,
+            sampling_rate,
+            self.hop_length,
+            self.f0_min,
+            self.f0_max,
+            pad=True,
+            model=self.model,
+            batch_size=1024,
+            device=x.device,
+            return_periodicity=True,
+        )
+
+        # Filter, remove silence, set uv threshold, refer to the original warehouse readme
+        if self.use_fast_filters:
+            pd = self.median_filter(pd)
+        else:
+            pd = torchcrepe.filter.median(pd, 3)
+
+        pd = torchcrepe.threshold.Silence(-60.0)(pd, x, sampling_rate, 512)
+        f0 = torchcrepe.threshold.At(self.threshold)(f0, pd)
+        
+        if self.use_fast_filters:
+            f0 = self.mean_filter(f0)
+        else:
+            f0 = torchcrepe.filter.mean(f0, 3)
+
+        f0 = torch.where(torch.isnan(f0), torch.full_like(f0, 0), f0)[0]
+
+        return self.post_process(x, sampling_rate, f0, pad_to)